phi3.py

from typing import List
from typing import Optional
from typing import Tuple
from typing import Union

import torch

from transformers.modeling_outputs import BaseModelOutputWithPast

from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
from liger_kernel.transformers.model.output_classes import LigerCausalLMOutputWithPast


def lce_forward(
    self,
    input_ids: torch.LongTensor = None,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_values: Optional[List[torch.FloatTensor]] = None,
    inputs_embeds: Optional[torch.FloatTensor] = None,
    labels: Optional[torch.LongTensor] = None,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
    cache_position: Optional[torch.LongTensor] = None,
    logits_to_keep: Union[int, torch.Tensor] = 0,
    skip_logits: Optional[bool] = None,
    **kwargs,
) -> Union[Tuple, LigerCausalLMOutputWithPast]:
    r"""
    Example:

    ```python
    >>> from transformers import AutoTokenizer, Phi3ForCausalLM

    >>> model = Phi3ForCausalLM.from_pretrained("meta-phi3/Phi3-2-7b-hf")
    >>> tokenizer = AutoTokenizer.from_pretrained("meta-phi3/Phi3-2-7b-hf")

    >>> prompt = "Hey, are you conscious? Can you talk to me?"
    >>> inputs = tokenizer(prompt, return_tensors="pt")

    >>> # Generate
    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
    ```"""

    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
    output_hidden_states = (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    outputs: BaseModelOutputWithPast = self.model(
        input_ids=input_ids,
        attention_mask=attention_mask,
        position_ids=position_ids,
        past_key_values=past_key_values,
        inputs_embeds=inputs_embeds,
        use_cache=use_cache,
        cache_position=cache_position,
        **kwargs,
    )

    hidden_states = outputs.last_hidden_state
    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
    slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
    kept_hidden_states = hidden_states[:, slice_indices, :]

    shift_labels = kwargs.pop("shift_labels", None)
    logits = None
    loss = None
    token_accuracy = None
    predicted_tokens = None

    if skip_logits and labels is None and shift_labels is None:
        raise ValueError("skip_logits is True, but labels and shift_labels are None")

    if skip_logits is None:
        # By default, if in training mode, don't materialize logits
        skip_logits = self.training and (labels is not None or shift_labels is not None)

    # Compute loss
    if skip_logits:
        result = LigerForCausalLMLoss(
            hidden_states=kept_hidden_states,
            lm_head_weight=self.lm_head.weight,
            labels=labels,
            shift_labels=shift_labels,
            hidden_size=self.config.hidden_size,
            **kwargs,
        )
        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
    else:
        logits = self.lm_head(kept_hidden_states)
        if labels is not None or shift_labels is not None:
            loss = self.loss_function(
                logits=logits,
                labels=labels,
                shift_labels=shift_labels,
                vocab_size=self.config.vocab_size,
                **kwargs,
            )

    if not return_dict:
        output_tuple = (logits,) + outputs[1:]
        output = (loss,) + output_tuple if loss is not None else output_tuple
        output = output + (token_accuracy,) if token_accuracy is not None else output
        output = output + (predicted_tokens,) if predicted_tokens is not None else output
        return output

    # Return custom output class with token_accuracy field
    return LigerCausalLMOutputWithPast(
        loss=loss,
        logits=logits,
        past_key_values=outputs.past_key_values,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
        token_accuracy=token_accuracy,
        predicted_tokens=predicted_tokens,
    )